Intuit is looking to hire an ML Engineer to help conceive, code, and deploy data science models at scale using the latest industry tools. The role involves data wrangling, feature engineering, developing models, and testing metrics to improve customer benefits and ease pain points in the data science process.
Requirements
- Knowledgeable with Data Science tools and frameworks (i.e. Python, Scikit, NLTK, Numpy, Pandas, TensorFlow, Keras, R, Spark).
- Basic knowledge of machine learning techniques (i.e. classification, regression, and clustering).
- high-performance data processing
- machine learning algorithm exploration
- understand the details of the data being used and provide metrics to compare models.
- understand the specifics of the algorithm implementation in order to enhance it.
- build a tool for a specific project, or multiple projects though generally these types of projects are decoupled from any one project.
Responsibilities
- Discover data sources, get access to them, import them, clean them up, and make them “machine learning ready”.
- Work with data scientists to create and refine features from the underlying data and build pipelines to train and deploy models.
- Partner with data scientists to understand, implement, refine and design machine learning and other algorithms.
- Run regular A/B tests, gather data, perform statistical analysis, draw conclusions on the impact of your models.
- Explore new technology shifts in order to determine how they might connect with the customer benefits we wish to deliver.
- Build prototype models alongside data scientists. This may involve data exploration, high-performance data processing, and machine learning algorithm exploration.
- Works with data scientists to productionalize prototype models to the point where it can be used by customers at scale.
Other
- Work cross functionally with product managers, data scientists and product engineers, and communicate results to peers and leaders.
- BS, MS, or PhD degree in Computer Science or related field, or equivalent practical experience.
